The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clus- tering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Gr...
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The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clus- tering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, greedy algorithm substitutes for R*-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbi- trary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.
With the rapid development of digitalization, it is particularly important to provide superstores with accurate information on market demand and trends, and to help them develop reasonable replenishment plans and pric...
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There are some problems about secondary development tools of AutoC *** reading and transforming DXF files,it comes to be unreasonable order and invisible graphs. This paper gives a greedy algorithm to optimize graphic...
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ISBN:
(纸本)9781457708596
There are some problems about secondary development tools of AutoC *** reading and transforming DXF files,it comes to be unreasonable order and invisible graphs. This paper gives a greedy algorithm to optimize graphic trajectory, achieves its dynamic display by using Open GL technology, and clearly reveals the order of graphic elements and the internal control points. It proves good application effect in the process and analysis of the instances.
The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise(DBSCAN)(Ester et al.,1996),and has the following advantages: first,greedy al...
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The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise(DBSCAN)(Ester et al.,1996),and has the following advantages: first,greedy algorithm substitutes for R*-tree(Bechmann et al.,1990)in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second,the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally,authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.
The level of vehicle equipment warehouse support is a key factor in realizing the effective protection of our military vehicle equipment and enhancing the combat effectiveness of our *** level of vehicle equipment war...
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ISBN:
(纸本)9781450371445
The level of vehicle equipment warehouse support is a key factor in realizing the effective protection of our military vehicle equipment and enhancing the combat effectiveness of our *** level of vehicle equipment warehouse security is determined by whether the warehouse inventory structure is *** this paper,we emphatically analyzed the relevant conception of inventory equipment security rate and vehicle equipment warehouse security level,established an optimization model of vehicle equipment warehouse security level,and used Monte Carlo simulation with greedy algorithm to obtain relative optimal *** has important reference significance and application value to solve the problem of vehicle equipment warehouse procurement strategy and inventory optimization.
We study the optimal sampling set selection problem in sampling a noisy k-bandlimited graph signal. To minimize the effect of noise when trying to reconstruct a k-bandlimited graph signal from m samples, the optimal s...
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ISBN:
(纸本)9781538646595
We study the optimal sampling set selection problem in sampling a noisy k-bandlimited graph signal. To minimize the effect of noise when trying to reconstruct a k-bandlimited graph signal from m samples, the optimal sampling set selection problem has been shown to be equivalent to finding a m × k submatrix with the maximum smallest singular value, σ_(min) [3]. As the problem is NP-hard, we present a greedy algorithm inspired by a similar submatrix selection problem known in computer science and to which we add a local search refinement. We show that 1) in experiments, our algorithm finds a submatrix with larger σ_(min) than prior greedy algorithm [3], and 2) has a proven worst-case approximation ratio of 1/(1 + ε)k, where ε is a constant.
In this thesis, we suggest a new algorithm for solving convex optimization problems in Banach spaces. This algorithm is based on a greedy strategy, and it could be viewed as a nonlinear conjugate gradient type method....
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In this thesis, we suggest a new algorithm for solving convex optimization problems in Banach spaces. This algorithm is based on a greedy strategy, and it could be viewed as a nonlinear conjugate gradient type method. We prove its convergent rates under a suitable behavior of the modulus of uniform smoothness of the objective function. We apply the proposed algorithm on several examples such as approximation in Hilbert spaces, solving linear systems, and others. We also perform several numerical tests in the case when the objective function is the opposite of the log-likelihood function under the Logistic Regression model. Our numerical results confirm the fast convergence rate of the proposed algorithm and its potential for solving real life problems.
Focus on the scheduling problem of distributed computing tasks in Internet of Vehicles. Firstly, based on the computing-aware network theory, a distributed computing resource model of the Internet of Vehicles is estab...
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Focus on the scheduling problem of distributed computing tasks in Internet of Vehicles. Firstly, based on the computing-aware network theory, a distributed computing resource model of the Internet of Vehicles is established, and the seven-dimensional QoS attributes of the computing resources in the Internet of Vehicles (reliability between computing resources, communication costs, computing speed and computing costs of the computing resources themselves , computing energy consumption, computing stability, and computing success rate) are grouped and transformed into two-dimensional comprehensive attribute priorities: computing performance priority and communication performance priority. Secondly, the weighted directed acyclic graph model of distributed computing tasks in the Internet of Vehicles and the seven-dimensional QoS attribute weighted undirected topology graph model of distributed computing resources in the Internet of Vehicles are respectively established. Moreover, a dynamic greedy algorithm-based task of loading and computing resource scheduling algorithm is proposed. Finally, the example analysis shows that the overall performance of this dynamic greedy algorithm-based task of loading and computing resource scheduling algorithm is better than the classic HEFT scheduling algorithm and round robin scheduling algorithm.
U.S. Nuclear Regulatory Commission NUREG-0800, "Standard Review Plan," Section 3.7.1, "Seismic Parameters," Revision 4, describes acceptance criteria for seismic design time histories, including re...
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U.S. Nuclear Regulatory Commission NUREG-0800, "Standard Review Plan," Section 3.7.1, "Seismic Parameters," Revision 4, describes acceptance criteria for seismic design time histories, including response spectrum matching and checking power spectral density functions. This paper is a focused introduction of algorithm developed for response spectrum matching, named as the greedy Wavelet Method (GWM), shows substantial advances in convergence characteristics over the RspMatch09 program. RspMatch09 is used by the nuclear industry and represents the results of multiple iterations of enhancement by international researchers since the basic method was first introduced in 1978. GWM modifies a seed acceleration time by adding just one wavelet in each iteration, in contrast to RspMathc09 that adds a set of weighted wavelets, the weights determined by solving an optimization problem in each iteration. GWM includes procedures address two specific issues leading to convergence difficulties in response spectrum matching: The shape wavelets can be numerically distorted at high frequencies and the wavelet lead time previously recommended not sufficiently long. These newly developed procedures made GWM unconditionally stable. For the benchmark problem, GWM was demonstrated to save 99.5% of the wavelets required by RspMatch09. We also implemented in GWM several advanced, interactive tools to perform baseline corrections and other related tasks. Examples provided in this paper are for demonstration purposes only.
We describe a curious dynamical system that results in sequences of real numbers in [0, 1] with seemingly remarkable properties. Let the even function f : T -> R satisfy (f) over cap (k) >= c vertical bar k vert...
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We describe a curious dynamical system that results in sequences of real numbers in [0, 1] with seemingly remarkable properties. Let the even function f : T -> R satisfy (f) over cap (k) >= c vertical bar k vertical bar(-2) and define a sequence via x(n) = arg min (x) Sigma(n-1)(k=1)f(x - x(k)). Such sequences (X-n)(n=)(infinity)(1) seem to be astonishingly regularly distributed in various ways (satisfying favorable exponential sum estimates;every interval J subset of [0, 1] contains similar to vertical bar J vertical bar n elements). We prove W-2 (mu, nu) <= c/root n, where mu = 1/n Sigma(n)(k=1) delta(xk) is the empirical distribution, nu = dx is the Lebesgue measure and W-2(mu, nu) is the 2-Wasserstein distance between these two. Much stronger results seem to be true and it is an interesting problem to understand this dynamical system better. We obtain optimal results in dimension d >= 3: using G(x, y) to denote the Green's function of the Laplacian on a compact manifold, we show that x(n) = arg min(x is an element of M )Sigma(n-1)(k=1) G(x, x(k)) satisfies W-2 (1/n Sigma(n)(k=1) delta(xk), dx) less than or similar to 1/n(1/d). (C) 2020 Elsevier Inc. All rights reserved.
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